Comment by gymbeaux
16 hours ago
Those numbers are abysmal. Should we really be using LLMs to write our code? I have a theory- LLMs can spit out code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time. An enterprise app developed entirely with LLM-happy devs might end up virtually unmaintainable.
I’m not sure how to explain it, but the more I see LLM-written code the more I feel it’s bad code doing a good job of masquerading as good code. I think this take will become less-hot in the next year or two when we see enterprise greenfield projects that were created entirely with LLM “assistance” go to prod. I think we’ll find that the code is difficult for humans to read, understand, debug, and extend- and I think the larger the codebase the harder it will be for LLMs to maintain. More opportunity for hallucination, larger context windows needed, more tokens bought and spent for smaller and smaller code changes. I think the more code an LLM writes for an app, the worse that codebase becomes.
I can't help but feel that people continually underestimate how bad human written code becomes over time. The exception is probably single-person passion projects or open source projects that maintain quality governance over time.
I strongly suspect most closed source code developed under commercial or internal pressure is pretty awful after a few years of development.
All LLM code has to do is suck less than existing code. And that's presuming the code quality doesn't improve as the models, the harnesses and our ways of working with them improve.
Sucky human-written code is still based on human understanding, which can change over time, be readjusted or solidified. People implement something wrong once, then update their perspective, then in the future does it right.
LLMs doesn't have this benefit. You forget to add the correct to the system prompt, and the LLM will repeat the same mistake over and over, and worse than that, their mistakes aren't based on their understanding, it's basically random guesses.
Humans, even bad coders, still seem to have some sort of architecture in mind, even if it's spaghetti, whereas LLMs (obviously) don't think more than a few steps, and never about the full scope of what they're contributing too, and on purpose too, because you want the context to be as small as possible when you work with LLMs.
With LLMs you need to thread carefully between "What does the LLM need to know?" and "Can I skip passing this to the LLM this time?" while a human you can more or less dump them everything you sit on, and let them shift it through, and they'll mostly make it out OK.
> their mistakes aren't based on their understanding, it's basically random guesses
Whilst I don't claim any true "understanding" as that is a very loaded term that doesn't mean it's just random guesses.
Anyone using recent LLM coding agents on a regular basis would probably agree that there's something going on that fits some non-athropomorphizing, non-sentience-assigning definition of "understanding"
As for the point about improvement - I think that's an orthogonal issue to the overall code quality. With regard to human codebases - there's plenty of scenarios that negate the improvement of individuals. We're comparing organizations with LLMs - not individuals with LLMs and that makes a significant difference.
> while a human you can more or less dump them everything you sit on, and let them shift it through, and they'll mostly make it out OK
i dont see why software engineers are paid so well, and are so hard to hire?
just dump a bunch of requirements on a homeless person and itll just work out
I think the real issue might be that how “good” the code is matters less than being able to form a mental model for what the human who wrote the code was “thinking”. If written by a machine, this contract is broken and we get more confused, even if our traditional methods of evaluating the code come out equal.
That doesn’t help the developers who have high standards.
Yes. But that's not the point I'm addressing.
And where do you think the LLM learned coding from?
But anyway, let the LLM verify the code to give advice on improvements but don't let it write code unverified. That's my opinion on it anyway.
I've been sent code from vendors that didn't even compile, long before llms were a thing. Most shops that aren't primarily software have really really terrible software.
Not my observation. If you never look at the code and dont have basic guardrails in place (linters, architecture tests, some guidelines for best practices) - probably.
But as soon as you do minimal reviews and high-level corrections, applications turn out just fine.
Can there be bugs? Sure. That's the price of not reading or understanding every line. It should depend on the criticality of your software how much of these you tolerate and how much you don't (reviewing, understanding, testing everything 100% like you were used to if you had written it yourself will kill most if not all of your gained speed)
But I never got the impression of unmaintainability or unfixable bugs.
Actually the other side around: A really good cleanup pass, architectural changes, or bugfixes are seldom more than a few prompts and 2 hours away, provided your overall base is decent and you actually gave a fuck from the start.
> Can there be bugs? Sure. That's the price of not reading or understanding every line.
I've yet to come across a human developer who's output would meet this standard, despite writing every line.
In fact, having an LLM review our code is catching quite a few bugs before it reaches QA.
Indeed, though I find the distribution is different.
The humans may skip unit tests and need reminding; the AI always write unit tests once it's in AGENTS.md or whatever, but my experience* was that 5-10% of the time the LLM's attempt at a "test" would, instead of executing the code and examining the results, open the source code as a text file and run a regex to find/exclude certain substrings.
* At the start of this year, because Anthropic and OpenAI were both offering free trials. IDK how much things have changed since then, some things change fast in this domain, other things don't.
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Take a look at a sufficiently old random internal repo which was not written with LLMs and compare.
My observation is that they are equally bad and hard to maintain or even more so than the new ones.
One thing I’ve noticed is that the LLM assisted ones have a lot more comments which is nice but take more time to read.
Yes, LLMs generate technical debt.
And they do it faster than any human developer.
I have a theory that LLM generated code in a highly modular style (simple data, pure functions) will be easier to “recover” by a human team when the LLM gets muddled. So Haskell, basically.
> code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time
They clearly are only assistants for the moment, you can use them to do work ... but only if you could do the said work yourself alone in the first place.
I would say "only if you can review said work yourself alone", rather than "do".
I'm an experienced developer, but I don't count myself as a web dev or a python dev; I can review the web and python stuff I get out of the AI (sometimes I need to ask the AI follow-up questions so I can find official documentation for what it did), but I can't write it.
I think you could eventually do it then, it would just take you longer.
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Easy fix: Code's basically free now, so just pipe your errors straight into an LLM and get instant patches. Sure, the patches themselves are broken too, but no worries! just pipe those back in again. Code's disposable now, fresh code generated on every request.
On a more serious note, I think the problem will be the inability to handle/maintain the systems once they are too big and nobody has no idea what's inside of them or what they do.
Yeah, it’s so easy to generate code that you can do a whole codebase rewrite in a day.
Is this a good idea? Probably not—in the past we would only do that when the architecture was causing serious problems since it always has tons of behaviors that will accidentally not get carried forward, some of which are load bearing and will cause bugs.
Now we can do it in an afternoon and get the same long term bug behavior.
Have you worked with enterprise apps? The ones I have used for decades are hot garbages.
Now imagine decades of LLM code. Extrapolating the rate of increase of LoC, the source code ain't gonna fit on hard drives anymore.
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